Realtime optimization of multidimensional NMR spectroscopy on embedded sensing devices

A Monte Carlo algorithm to improve the measurement efficiency of low-field nuclear magnetic resonance

Nuclear magnetic resonance (NMR) has shown good applications in engineering fields such as well logging and rubber material ageing assessment. However, due to the low magnetic field strength of NMR sensors and the complex working conditions of engineering sites, the signal-to-noise ratio (SNR) of NMR signals is low, and it is usually necessary to increase the number of repeated measurements to improve the SNR, which means a longer measurement time. Therefore, it is especially important to set the measurement parameters appropriately for onsite NMR. In this paper, we propose a stochastic simulation using Monte Carlo methods to predict the measurement curves of [Formula: see text] and [Formula: see text] and correct the measurement parameters of the next step according to the previous measurement results. The method can update the measurement parameters in real time and perform automatic measurements. At the same time, this method greatly reduces the measurement time. The experimental results show that the method is suitable for the measurement of the self-diffusion coefficient D₀ and longitudinal relaxation time T₁, which are frequently used in NMR measurements.

Adaptive control for downhole nuclear magnetic resonance excitation

Nuclear magnetic resonance (NMR) measurements are performed with the pulse sequence and acquisition parameters set by the operator, which cannot be adjusted in real time according to sample characteristics. In one acquisition cycle, usually thousands of high-power pulses are transmitted and thousands of echo points are acquired. The power consumption cause the RF amplifier to overheat, and large amounts of acquired data may be invalid. Therefore, the optimization of excitation and acquisition processes is necessary to improve measurement efficiency. We explore a scheme for the real-time measurement of the samples by adaptively regulating the pulse sequence, which adapts the variable TE pulse sequence as the reconnaissance mode. The appropriate pulse sequence and reasonable parameters (NE, TE) can be selected according to the relaxation characteristics of the samples.This adaptive control strategy has great significance in guiding both dynamic and static measurements, and it is especially suitable for occasions where low magnetic field gradients and diffusion terms can be ignored. We also design a test circuit for adaptive control, which has the function of automatic parameter adjustment. By adjusting parameters such as the number of refocusing pulses, echo spacing, etc., the effective measurement of the samples can be achieved in practice.

T2 analysis using artificial neural networks

Quantitative analysis of magnetic resonance signal lifetimes could reveal molecular scale information. However, it is non-trivial to recover the relaxation times from MR experiments in the multi-component exponential decay analysis. Constraints are required for the ill-posed problem in conventional inversion methods, which could lead to biased solutions. Artificial neural networks (ANNs) are a series of densely connected information processing nodes which cumulatively map a set of inputs to a set of outputs. They have proven to be universal approximators and powerful tools for solving complex nonlinear problems. In this work, ANNs were trained to recover T₂ relaxation times. Both the discrete T₂ spectrum and continuous T₂ distribution were considered. Increased accuracy was achieved compared to the traditional methods. The continuous spectrum peak widths, generally not reliable in the traditional approach, could be determined accurately with ANN when the signal-to-noise ratio permitted.